Development of Crime Reporting System to Identify Patterns of Crime in Laguna
Purpose – This study developed an online crime reporting system that uses artificial intelligence to analyze crime incident reports to provide up-to-date crime statistics, map crime hot locations, and manage dynamic databases.
Method – The knowledge discovery process in databases (KDD) was utilized for the model development. Scrum, an agile development technique, proved helpful in the iterative and gradual development of the system. In addition, several ICT technologies were applied, such as geo-mapping and pattern analysis utilizing the data mining technique. The user evaluation tool was composed of Technology Acceptance Model for the criteria and ISO/IEC 25010 software metrics for the sub-criteria.
Results – Based on the patterns generated from the criminal records data set, the researchers used machine learning in a prediction model generated using the Decision Tree algorithm, revealing several important insights about the incidences of non-index crimes in Laguna. The findings suggest that date, time, and location factors are the best predictors of crime occurrences. Moreover, the researchers agree with the respondents' comments and suggestions that the crime map should include a variety of graphical representations such as a table ranking the crime rates from highest to lowest and a pie graph showing the comparable data of analytics crime per town and cities to make the system more interesting to any type of user. This is true for the crime analysis website's crime map for public access user review, which ultimately came out to be acceptable.
Conclusion – The online crime reporting system provides various functions and features for various users. This can be used to raise people's awareness regarding dangerous locations and help agencies predict future crime in a specific location within a particular time.
Recommendations – PNP-Laguna would gain the most from the project, it is recommended that the PNP-Laguna, municipal police stations, and the LSPU work efficiently together to continue expanding the crime analysis website. The website offers analytics for decision-making support in addition to covering crime management information systems across police agencies for decentralization. Continued experimentation on index and non-index crime datasets to develop intelligent systems capable of forecasting must be explored, as the index crime dataset demonstrated a highly good result in the project.
Research Implications – The academe and local law enforcement agencies must collaboratively develop strategies that reinforce the importance of community engagement. The system provides public access through geo-mapping that supports the community, or locally based crime prevention, instead of targeting individuals, targets areas where the risks of becoming involved in crime or being victimized are high.
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